#' Control functions for Kaplan-Meier plot annotation tables

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Auxiliary functions for controlling arguments for formatting the annotation tables that can be added to plots

#' generated via [g_km()].

#'

#' @param x (`proportion`)\cr x-coordinate for center of annotation table.

#' @param y (`proportion`)\cr y-coordinate for center of annotation table.

#' @param w (`proportion`)\cr relative width of the annotation table.

#' @param h (`proportion`)\cr relative height of the annotation table.

#' @param fill (`flag` or `character`)\cr whether the annotation table should have a background fill color.

#'   Can also be a color code to use as the background fill color. If `TRUE`, color code defaults to `"#00000020"`.

#'

#' @return A list of components with the same names as the arguments.

#'

#' @seealso [g_km()]

#'

#' @name control_annot

NULL

#' @describeIn control_annot Control function for formatting the median survival time annotation table. This annotation

#'   table can be added in [g_km()] by setting `annot_surv_med=TRUE`, and can be configured using the

#'   `control_surv_med_annot()` function by setting it as the `control_annot_surv_med` argument.

#'

#' @examples

#' control_surv_med_annot()

#'

#' @export

control_surv_med_annot <- function(x = 0.8, y = 0.85, w = 0.32, h = 0.16, fill = TRUE) {

}

#' @describeIn control_annot Control function for formatting the Cox-PH annotation table. This annotation table can be

#'   added in [g_km()] by setting `annot_coxph=TRUE`, and can be configured using the `control_coxph_annot()` function

#'   by setting it as the `control_annot_coxph` argument.

#'

#' @param ref_lbls (`flag`)\cr whether the reference group should be explicitly printed in labels for the

#'   annotation table. If `FALSE` (default), only comparison groups will be printed in the table labels.

#'

#' @examples

#' control_coxph_annot()

#'

#' @export

control_coxph_annot <- function(x = 0.29, y = 0.51, w = 0.4, h = 0.125, fill = TRUE, ref_lbls = FALSE) {

}

#' Helper function to calculate x-tick positions

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Calculate the positions of ticks on the x-axis. However, if `xticks` already

#' exists it is kept as is. It is based on the same function `ggplot2` relies on,

#' and is required in the graphic and the patient-at-risk annotation table.

#'

#' @inheritParams g_km

#' @inheritParams h_ggkm

#'

#' @return A vector of positions to use for x-axis ticks on a `ggplot` object.

#'

#' @examples

#' library(dplyr)

#' library(survival)

#'

#' data <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%

#'   h_data_plot()

#'

#' h_xticks(data)

#' h_xticks(data, xticks = seq(0, 3000, 500))

#' h_xticks(data, xticks = 500)

#' h_xticks(data, xticks = 500, max_time = 6000)

#' h_xticks(data, xticks = c(0, 500), max_time = 300)

#' h_xticks(data, xticks = 500, max_time = 300)

#'

#' @export

h_xticks <- function(data, xticks = NULL, max_time = NULL) {

    } else {

    }

    } else {

    }

  } else {

      )

    )

  }

}

#' Helper function for survival estimations

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Transform a survival fit to a table with groups in rows characterized by N, median and confidence interval.

#'

#' @inheritParams h_data_plot

#'

#' @return A summary table with statistics `N`, `Median`, and `XX% CI` (`XX` taken from `fit_km`).

#'

#' @examples

#' library(dplyr)

#' library(survival)

#'

#' adtte <- tern_ex_adtte %>% filter(PARAMCD == "OS")

#' fit <- survfit(

#'   formula = Surv(AVAL, 1 - CNSR) ~ ARMCD,

#'   data = adtte

#' )

#' h_tbl_median_surv(fit_km = fit)

#'

#' @export

h_tbl_median_surv <- function(fit_km, armval = "All") {

  } else {

  }

  )

  )

}

#' Helper function for generating a pairwise Cox-PH table

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Create a `data.frame` of pairwise stratified or unstratified Cox-PH analysis results.

#'

#' @inheritParams g_km

#' @param annot_coxph_ref_lbls (`flag`)\cr whether the reference group should be explicitly printed in labels for the

#'   `annot_coxph` table. If `FALSE` (default), only comparison groups will be printed in `annot_coxph` table labels.

#'

#' @return A `data.frame` containing statistics `HR`, `XX% CI` (`XX` taken from `control_coxph_pw`),

#'   and `p-value (log-rank)`.

#'

#' @examples

#' library(dplyr)

#'

#' adtte <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   mutate(is_event = CNSR == 0)

#'

#' h_tbl_coxph_pairwise(

#'   df = adtte,

#'   variables = list(tte = "AVAL", is_event = "is_event", arm = "ARM"),

#'   control_coxph_pw = control_coxph(conf_level = 0.9)

#' )

#'

#' @export

h_tbl_coxph_pairwise <- function(df,

                                 variables,

                                 ref_group_coxph = NULL,

                                 control_coxph_pw = control_coxph(),

                                 annot_coxph_ref_lbls = FALSE) {

    )

  }

    )

      ),

    )

}

#' Helper function to tidy survival fit data

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Convert the survival fit data into a data frame designed for plotting

#' within `g_km`.

#'

#' This starts from the [broom::tidy()] result, and then:

#'   * Post-processes the `strata` column into a factor.

#'   * Extends each stratum by an additional first row with time 0 and probability 1 so that

#'     downstream plot lines start at those coordinates.

#'   * Adds a `censor` column.

#'   * Filters the rows before `max_time`.

#'

#' @inheritParams g_km

#' @param fit_km (`survfit`)\cr result of [survival::survfit()].

#' @param armval (`string`)\cr used as strata name when treatment arm variable only has one level. Default is `"All"`.

#'

#' @return A `tibble` with columns `time`, `n.risk`, `n.event`, `n.censor`, `estimate`, `std.error`, `conf.high`,

#'   `conf.low`, `strata`, and `censor`.

#'

#' @examples

#' library(dplyr)

#' library(survival)

#'

#' # Test with multiple arms

#' tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%

#'   h_data_plot()

#'

#' # Test with single arm

#' tern_ex_adtte %>%

#'   filter(PARAMCD == "OS", ARMCD == "ARM B") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%

#'   h_data_plot(armval = "ARM B")

#'

#' @export

h_data_plot <- function(fit_km,

                        armval = "All",

                        max_time = NULL) {

    )

  } else {

  }

      )

    }

  )

  }

}

## Deprecated Functions ----

#' Helper function to create a KM plot

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' Draw the Kaplan-Meier plot using `ggplot2`.

#'

#' @inheritParams g_km

#' @param data (`data.frame`)\cr survival data as pre-processed by `h_data_plot`.

#'

#' @return A `ggplot` object.

#'

#' @examples

#' \donttest{

#' library(dplyr)

#' library(survival)

#'

#' fit_km <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)

#' data_plot <- h_data_plot(fit_km = fit_km)

#' xticks <- h_xticks(data = data_plot)

#' gg <- h_ggkm(

#'   data = data_plot,

#'   censor_show = TRUE,

#'   xticks = xticks,

#'   xlab = "Days",

#'   yval = "Survival",

#'   ylab = "Survival Probability",

#'   title = "Survival"

#' )

#' gg

#' }

#'

#' @export

h_ggkm <- function(data,

                   xticks = NULL,

                   yval = "Survival",

                   censor_show,

                   xlab,

                   ylab,

                   ylim = NULL,

                   title,

                   footnotes = NULL,

                   max_time = NULL,

                   lwd = 1,

                   lty = NULL,

                   pch = 3,

                   size = 2,

                   col = NULL,

                   ci_ribbon = FALSE,

                   ggtheme = nestcolor::theme_nest()) {

  )

    } else {

    }

  }

  # change estimates of survival to estimates of failure (1 - survival)

  }

      )

    ) +

  }

  }

  }

  }

      ),

    ) +

      )

  }

    } else {

    }

  }

  }

  )

}

#' `ggplot` decomposition

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' The elements composing the `ggplot` are extracted and organized in a `list`.

#'

#' @param gg (`ggplot`)\cr a graphic to decompose.

#'

#' @return A named `list` with elements:

#'   * `panel`: The panel.

#'   * `yaxis`: The y-axis.

#'   * `xaxis`: The x-axis.

#'   * `xlab`: The x-axis label.

#'   * `ylab`: The y-axis label.

#'   * `guide`: The legend.

#'

#' @examples

#' \donttest{

#' library(dplyr)

#' library(survival)

#' library(grid)

#'

#' fit_km <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)

#' data_plot <- h_data_plot(fit_km = fit_km)

#' xticks <- h_xticks(data = data_plot)

#' gg <- h_ggkm(

#'   data = data_plot,

#'   yval = "Survival",

#'   censor_show = TRUE,

#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",

#'   title = "tt",

#'   footnotes = "ff"

#' )

#'

#' g_el <- h_decompose_gg(gg)

#' grid::grid.newpage()

#' grid.rect(gp = grid::gpar(lty = 1, col = "red", fill = "gray85", lwd = 5))

#' grid::grid.draw(g_el$panel)

#'

#' grid::grid.newpage()

#' grid.rect(gp = grid::gpar(lty = 1, col = "royalblue", fill = "gray85", lwd = 5))

#' grid::grid.draw(with(g_el, cbind(ylab, yaxis)))

#' }

#'

#' @export

h_decompose_gg <- function(gg) {

  )

  )

}

#' Helper function to prepare a KM layout

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' Prepares a (5 rows) x (2 cols) layout for the Kaplan-Meier curve.

#'

#' @inheritParams g_km

#' @inheritParams h_ggkm

#' @param g_el (`list` of `gtable`)\cr list as obtained by `h_decompose_gg()`.

#' @param annot_at_risk (`flag`)\cr compute and add the annotation table reporting the number of

#'   patient at risk matching the main grid of the Kaplan-Meier curve.

#'

#' @return A grid layout.

#'

#' @details The layout corresponds to a grid of two columns and five rows of unequal dimensions. Most of the

#'   dimension are fixed, only the curve is flexible and will accommodate with the remaining free space.

#'   * The left column gets the annotation of the `ggplot` (y-axis) and the names of the strata for the patient

#'     at risk tabulation. The main constraint is about the width of the columns which must allow the writing of

#'     the strata name.

#'   * The right column receive the `ggplot`, the legend, the x-axis and the patient at risk table.

#'

#' @examples

#' \donttest{

#' library(dplyr)

#' library(survival)

#' library(grid)

#'

#' fit_km <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)

#' data_plot <- h_data_plot(fit_km = fit_km)

#' xticks <- h_xticks(data = data_plot)

#' gg <- h_ggkm(

#'   data = data_plot,

#'   censor_show = TRUE,

#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",

#'   title = "tt", footnotes = "ff", yval = "Survival"

#' )

#' g_el <- h_decompose_gg(gg)

#' lyt <- h_km_layout(data = data_plot, g_el = g_el, title = "t", footnotes = "f")

#' grid.show.layout(lyt)

#' }

#'

#' @export

h_km_layout <- function(data, g_el, title, footnotes, annot_at_risk = TRUE, annot_at_risk_title = TRUE) {

  )

        )

      )

    )

  )

  }

  )

  )

  }

    }

  } else {

  }

    )

  )

}

#' Helper function to create patient-at-risk grobs

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' Two graphical objects are obtained, one corresponding to row labeling and the second to the table of

#' numbers of patients at risk. If `title = TRUE`, a third object corresponding to the table title is

#' also obtained.

#'

#' @inheritParams g_km

#' @inheritParams h_ggkm

#' @param annot_tbl (`data.frame`)\cr annotation as prepared by [survival::summary.survfit()] which

#'   includes the number of patients at risk at given time points.

#' @param xlim (`numeric(1)`)\cr the maximum value on the x-axis (used to ensure the at risk table aligns with the KM

#'   graph).

#' @param title (`flag`)\cr whether the "Patients at Risk" title should be added above the `annot_at_risk`

#'   table. Has no effect if `annot_at_risk` is `FALSE`. Defaults to `TRUE`.

#'

#' @return A named `list` of two `gTree` objects if `title = FALSE`: `at_risk` and `label`, or three

#'   `gTree` objects if `title = TRUE`: `at_risk`, `label`, and `title`.

#'

#' @examples

#' \donttest{

#' library(dplyr)

#' library(survival)

#' library(grid)

#'

#' fit_km <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)

#'

#' data_plot <- h_data_plot(fit_km = fit_km)

#'

#' xticks <- h_xticks(data = data_plot)

#'

#' gg <- h_ggkm(

#'   data = data_plot,

#'   censor_show = TRUE,

#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",

#'   title = "tt", footnotes = "ff", yval = "Survival"

#' )

#'

#' # The annotation table reports the patient at risk for a given strata and

#' # times (`xticks`).

#' annot_tbl <- summary(fit_km, times = xticks)

#' if (is.null(fit_km$strata)) {

#'   annot_tbl <- with(annot_tbl, data.frame(n.risk = n.risk, time = time, strata = "All"))

#' } else {

#'   strata_lst <- strsplit(sub("=", "equals", levels(annot_tbl$strata)), "equals")

#'   levels(annot_tbl$strata) <- matrix(unlist(strata_lst), ncol = 2, byrow = TRUE)[, 2]

#'   annot_tbl <- data.frame(

#'     n.risk = annot_tbl$n.risk,

#'     time = annot_tbl$time,

#'     strata = annot_tbl$strata

#'   )

#' }

#'

#' # The annotation table is transformed into a grob.

#' tbl <- h_grob_tbl_at_risk(data = data_plot, annot_tbl = annot_tbl, xlim = max(xticks))

#'

#' # For the representation, the layout is estimated for which the decomposition

#' # of the graphic element is necessary.

#' g_el <- h_decompose_gg(gg)

#' lyt <- h_km_layout(data = data_plot, g_el = g_el, title = "t", footnotes = "f")

#'

#' grid::grid.newpage()

#' pushViewport(viewport(layout = lyt, height = .95, width = .95))

#' grid.rect(gp = grid::gpar(lty = 1, col = "purple", fill = "gray85", lwd = 1))

#' pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 2))

#' grid.rect(gp = grid::gpar(lty = 1, col = "orange", fill = "gray85", lwd = 1))

#' grid::grid.draw(tbl$at_risk)

#' popViewport()

#' pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 1))

#' grid.rect(gp = grid::gpar(lty = 1, col = "green3", fill = "gray85", lwd = 1))

#' grid::grid.draw(tbl$label)

#' }

#'

#' @export

h_grob_tbl_at_risk <- function(data, annot_tbl, xlim, title = TRUE) {

  )

      )

    )

  }

    ),

      ),

    )

  )

    ),

    )

  )

            ),

          )

        )

      )

    ),

            ),

          )

        )

      )

    )

  )

            ),

          )

        )

      )

    )

  }

}

#' Helper function to create survival estimation grobs

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' The survival fit is transformed in a grob containing a table with groups in

#' rows characterized by N, median and 95% confidence interval.

#'

#' @inheritParams g_km

#' @inheritParams h_data_plot

#' @param ttheme (`list`)\cr see [gridExtra::ttheme_default()].

#' @param x (`proportion`)\cr a value between 0 and 1 specifying x-location.

#' @param y (`proportion`)\cr a value between 0 and 1 specifying y-location.

#' @param width (`grid::unit`)\cr width (as a unit) to use when printing the grob.

#'

#' @return A `grob` of a table containing statistics `N`, `Median`, and `XX% CI` (`XX` taken from `fit_km`).

#'

#' @examples

#' \donttest{

#' library(dplyr)

#' library(survival)

#' library(grid)

#'

#' grid::grid.newpage()

#' grid.rect(gp = grid::gpar(lty = 1, col = "pink", fill = "gray85", lwd = 1))

#' tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%

#'   h_grob_median_surv() %>%

#'   grid::grid.draw()

#' }

#'

#' @export

h_grob_median_surv <- function(fit_km,

                               armval = "All",

                               x = 0.9,

                               y = 0.9,

                               width = grid::unit(0.3, "npc"),

                               ttheme = gridExtra::ttheme_default()) {

  )

  ))

  })

  })

  }

  )

  )

    )

  )

}

#' Helper function to create grid object with y-axis annotation

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' Build the y-axis annotation from a decomposed `ggplot`.

#'

#' @param ylab (`gtable`)\cr the y-lab as a graphical object derived from a `ggplot`.

#' @param yaxis (`gtable`)\cr the y-axis as a graphical object derived from a `ggplot`.

#'

#' @return A `gTree` object containing the y-axis annotation from a `ggplot`.

#'

#' @examples

#' \donttest{

#' library(dplyr)

#' library(survival)

#' library(grid)

#'

#' fit_km <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)

#' data_plot <- h_data_plot(fit_km = fit_km)

#' xticks <- h_xticks(data = data_plot)

#' gg <- h_ggkm(

#'   data = data_plot,

#'   censor_show = TRUE,

#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",

#'   title = "title", footnotes = "footnotes", yval = "Survival"

#' )

#'

#' g_el <- h_decompose_gg(gg)

#'

#' grid::grid.newpage()

#' pvp <- grid::plotViewport(margins = c(5, 4, 2, 20))

#' pushViewport(pvp)

#' grid::grid.draw(h_grob_y_annot(ylab = g_el$ylab, yaxis = g_el$yaxis))

#' grid.rect(gp = grid::gpar(lty = 1, col = "gray35", fill = NA))

#' }

#'

#' @export

h_grob_y_annot <- function(ylab, yaxis) {

  )

      ),

    )

  )

}

#' Helper function to create Cox-PH grobs

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' Grob of `rtable` output from [h_tbl_coxph_pairwise()]

#'

#' @inheritParams h_grob_median_surv

#' @param ... arguments to pass to [h_tbl_coxph_pairwise()].

#' @param x (`proportion`)\cr a value between 0 and 1 specifying x-location.

#' @param y (`proportion`)\cr a value between 0 and 1 specifying y-location.

#' @param width (`grid::unit`)\cr width (as a unit) to use when printing the grob.

#'

#' @return A `grob` of a table containing statistics `HR`, `XX% CI` (`XX` taken from `control_coxph_pw`),

#'   and `p-value (log-rank)`.

#'

#' @examples

#' \donttest{

#' library(dplyr)

#' library(survival)

#' library(grid)

#'

#' grid::grid.newpage()

#' grid.rect(gp = grid::gpar(lty = 1, col = "pink", fill = "gray85", lwd = 1))

#' data <- tern_ex_adtte %>%

#'   filter(PARAMCD == "OS") %>%

#'   mutate(is_event = CNSR == 0)

#' tbl_grob <- h_grob_coxph(

#'   df = data,

#'   variables = list(tte = "AVAL", is_event = "is_event", arm = "ARMCD"),

#'   control_coxph_pw = control_coxph(conf_level = 0.9), x = 0.5, y = 0.5

#' )

#' grid::grid.draw(tbl_grob)

#' }

#'

#' @export

h_grob_coxph <- function(...,

                         x = 0,

                         y = 0,

                         width = grid::unit(0.4, "npc"),

                         ttheme = gridExtra::ttheme_default(

                           padding = grid::unit(c(1, .5), "lines"),

                           core = list(bg_params = list(fill = c("grey95", "grey90"), alpha = .5))

                         )) {

  )

  ))

  })

  })

  }

      )

        )

      )

    },

      ))

          )

        )

      )

    }

  )

}

# Utility functions to cooperate with {rtables} package

#' Convert table into matrix of strings

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Helper function to use mostly within tests. `with_spaces`parameter allows

#' to test not only for content but also indentation and table structure.

#' `print_txt_to_copy` instead facilitate the testing development by returning a well

#' formatted text that needs only to be copied and pasted in the expected output.

#'

#' @inheritParams formatters::toString

#' @param x (`VTableTree`)\cr `rtables` table object.

#' @param with_spaces (`flag`)\cr whether the tested table should keep the indentation and other relevant spaces.

#' @param print_txt_to_copy (`flag`)\cr utility to have a way to copy the input table directly

#'   into the expected variable instead of copying it too manually.

#'

#' @return A `matrix` of `string`s. If `print_txt_to_copy = TRUE` the well formatted printout of the

#'   table will be printed to console, ready to be copied as a expected value.

#'

#' @examples

#' tbl <- basic_table() %>%

#'   split_rows_by("SEX") %>%

#'   split_cols_by("ARM") %>%

#'   analyze("AGE") %>%

#'   build_table(tern_ex_adsl)

#'

#' to_string_matrix(tbl, widths = ceiling(propose_column_widths(tbl) / 2))

#'

#' @export

to_string_matrix <- function(x, widths = NULL, max_width = NULL,

                             hsep = formatters::default_hsep(),

                             with_spaces = TRUE, print_txt_to_copy = FALSE) {

  } else {

  }

  }

  # Producing the matrix to test

  } else {

  }

  # Printing to console formatted output that needs to be copied in "expected"

    }

  }

  # Return values

}

#' Blank for missing input

#'

#' Helper function to use in tabulating model results.

#'

#' @param x (`vector`)\cr input for a cell.

#'

#' @return An empty `character` vector if all entries in `x` are missing (`NA`), otherwise

#'   the unlisted version of `x`.

#'

#' @keywords internal

unlist_and_blank_na <- function(x) {

  } else {

  }

}

#' Constructor for content functions given a data frame with flag input

#'

#' This can be useful for tabulating model results.

#'

#' @param analysis_var (`string`)\cr variable name for the column containing values to be returned by the

#'   content function.

#' @param flag_var (`string`)\cr variable name for the logical column identifying which row should be returned.

#' @param format (`string`)\cr `rtables` format to use.

#'

#' @return A content function which gives `df$analysis_var` at the row identified by

#'   `.df_row$flag` in the given format.

#'

#' @keywords internal

cfun_by_flag <- function(analysis_var,

                         flag_var,

                         format = "xx",

                         .indent_mods = NULL) {

    )

  }

}

#' Content row function to add row total to labels

#'

#' This takes the label of the latest row split level and adds the row total from `df` in parentheses.

#' This function differs from [c_label_n_alt()] by taking row counts from `df` rather than

#' `alt_counts_df`, and is used by [add_rowcounts()] when `alt_counts` is set to `FALSE`.

#'

#' @inheritParams argument_convention

#'

#' @return A list with formatted [rtables::CellValue()] with the row count value and the correct label.

#'

#' @note It is important here to not use `df` but rather `.N_row` in the implementation, because

#'   the former is already split by columns and will refer to the first column of the data only.

#'

#' @seealso [c_label_n_alt()] which performs the same function but retrieves row counts from

#'   `alt_counts_df` instead of `df`.

#'

#' @keywords internal

c_label_n <- function(df,

                      labelstr,

                      .N_row) { # nolint

  )

}

#' Content row function to add `alt_counts_df` row total to labels

#'

#' This takes the label of the latest row split level and adds the row total from `alt_counts_df`

#' in parentheses. This function differs from [c_label_n()] by taking row counts from `alt_counts_df`

#' rather than `df`, and is used by [add_rowcounts()] when `alt_counts` is set to `TRUE`.

#'

#' @inheritParams argument_convention

#'

#' @return A list with formatted [rtables::CellValue()] with the row count value and the correct label.

#'

#' @seealso [c_label_n()] which performs the same function but retrieves row counts from `df` instead

#'   of `alt_counts_df`.

#'

#' @keywords internal

c_label_n_alt <- function(df,

                          labelstr,

                          .alt_df_row) {

  )

}

#' Layout-creating function to add row total counts

#'

#' @description `r lifecycle::badge("stable")`

#'

#' This works analogously to [rtables::add_colcounts()] but on the rows. This function

#'  is a wrapper for [rtables::summarize_row_groups()].

#'

#' @inheritParams argument_convention

#' @param alt_counts (`flag`)\cr whether row counts should be taken from `alt_counts_df` (`TRUE`)

#'   or from `df` (`FALSE`). Defaults to `FALSE`.

#'

#' @return A modified layout where the latest row split labels now have the row-wise

#'   total counts (i.e. without column-based subsetting) attached in parentheses.

#'

#' @note Row count values are contained in these row count rows but are not displayed

#'   so that they are not considered zero rows by default when pruning.

#'

#' @examples

#' basic_table() %>%

#'   split_cols_by("ARM") %>%

#'   add_colcounts() %>%

#'   split_rows_by("RACE", split_fun = drop_split_levels) %>%

#'   add_rowcounts() %>%

#'   analyze("AGE", afun = list_wrap_x(summary), format = "xx.xx") %>%

#'   build_table(DM)

#'

#' @export

add_rowcounts <- function(lyt, alt_counts = FALSE) {

  )

}

#' Obtain column indices

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Helper function to extract column indices from a `VTableTree` for a given

#' vector of column names.

#'

#' @param table_tree (`VTableTree`)\cr `rtables` table object to extract the indices from.

#' @param col_names (`character`)\cr vector of column names.

#'

#' @return A vector of column indices.

#'

#' @export

h_col_indices <- function(table_tree, col_names) {

}

#' Labels or names of list elements

#'

#' Internal helper function for working with nested statistic function results which typically

#' don't have labels but names that we can use.

#'

#' @param x (`list`)\cr a list.

#'

#' @return A `character` vector with the labels or names for the list elements.

#'

#' @keywords internal

labels_or_names <- function(x) {

}

#' Convert to `rtable`

#'

#' @description `r lifecycle::badge("stable")`

#'

#' This is a new generic function to convert objects to `rtable` tables.

#'

#' @param x (`data.frame`)\cr the object which should be converted to an `rtable`.

#' @param ... additional arguments for methods.

#'

#' @return An `rtables` table object. Note that the concrete class will depend on the method used.

#'

#' @export

as.rtable <- function(x, ...) { # nolint

}

#' @describeIn as.rtable Method for converting a `data.frame` that contains numeric columns to `rtable`.

#'

#' @param format (`string` or `function`)\cr the format which should be used for the columns.

#'

#' @method as.rtable data.frame

#'

#' @examples

#' x <- data.frame(

#'   a = 1:10,

#'   b = rnorm(10)

#' )

#' as.rtable(x)

#'

#' @export

as.rtable.data.frame <- function(x, format = "xx.xx", ...) {

      ),

            )

          )

        },

      )

    )

  )

}

#' Split parameters

#'

#' @description `r lifecycle::badge("deprecated")`

#'

#' It divides the data in the vector `param` into the groups defined by `f` based on specified `values`. It is relevant

#' in `rtables` layers so as to distribute parameters `.stats` or' `.formats` into lists with items corresponding to

#' specific analysis function.

#'

#' @param param (`vector`)\cr the parameter to be split.

#' @param value (`vector`)\cr the value used to split.

#' @param f (`list`)\cr the reference to make the split.

#'

#' @return A named `list` with the same element names as `f`, each containing the elements specified in `.stats`.

#'

#' @examples

#' f <- list(

#'   surv = c("pt_at_risk", "event_free_rate", "rate_se", "rate_ci"),

#'   surv_diff = c("rate_diff", "rate_diff_ci", "ztest_pval")

#' )

#'

#' .stats <- c("pt_at_risk", "rate_diff")

#' h_split_param(.stats, .stats, f = f)

#'

#' # $surv

#' # [1] "pt_at_risk"

#' #

#' # $surv_diff

#' # [1] "rate_diff"

#'

#' .formats <- c("pt_at_risk" = "xx", "event_free_rate" = "xxx")

#' h_split_param(.formats, names(.formats), f = f)

#'

#' # $surv

#' # pt_at_risk event_free_rate

#' # "xx"           "xxx"

#' #

#' # $surv_diff

#' # NULL

#'

#' @export

h_split_param <- function(param,

                          value,

                          f) {

}

#' Get selected statistics names

#'

#' Helper function to be used for creating `afun`.

#'

#' @param .stats (`vector` or `NULL`)\cr input to the layout creating function. Note that `NULL` means

#'   in this context that all default statistics should be used.

#' @param all_stats (`character`)\cr all statistics which can be selected here potentially.

#'

#' @return A `character` vector with the selected statistics.

#'

#' @keywords internal

afun_selected_stats <- function(.stats, all_stats) {

  } else {

  }

}

#' Add variable labels to top left corner in table

#'

#' @description `r lifecycle::badge("stable")`

#'

#' Helper layout-creating function to append the variable labels of a given variables vector

#' from a given dataset in the top left corner. If a variable label is not found then the

#' variable name itself is used instead. Multiple variable labels are concatenated with slashes.

#'

#' @inheritParams argument_convention

#' @param vars (`character`)\cr variable names of which the labels are to be looked up in `df`.

#' @param indent (`integer(1)`)\cr non-negative number of nested indent space, default to 0L which means no indent.

#'   1L means two spaces indent, 2L means four spaces indent and so on.

#'

#' @return A modified layout with the new variable label(s) added to the top-left material.

#'

#' @note This is not an optimal implementation of course, since we are using here the data set